Enhanced clustering based routing protocol in vehicular ad‐hoc networks

Naeem, Afia; Rizwan, Muhammad; Alsubai, Shtwai; Almadhor, Ahmad; Akhtaruzzaman, Md.; Islam, Shayla

doi:10.1049/els2.12069

Cited by 22 publications

(10 citation statements)

References 62 publications

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“…Tracking limits are established based on the central point of the coordinate system, with two circles encompassing the outermost boundaries of the circular region 19 . Within this area, vehicles v1, v2, v3, and v4 navigate and move.…”

Section: Proposed Methodologymentioning

confidence: 99%

Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

Santhi,

Jacob,

Sheela

et al. 2024

Sci Rep

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In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.

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Section: Proposed Methodologymentioning

confidence: 99%

Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

Santhi,

Jacob,

Sheela

et al. 2024

Sci Rep

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show abstract

“…To ensure diversity and coverage, the app incorporates different driving conditions, such as urban, rural, and highway scenarios. This is why the data generated by the app is widely used by the research community 43 – 45 .…”

Section: Range and Azimuth Parametersmentioning

confidence: 99%

“…Moreover, the data generated are of real-time nature, and can directly be used to analyze the environment in real-time. It is because of this reason that the app has been widely used by the research community for validating their algorithms 43 – 45 . The radar sensor details are explained below.…”

Section: Range and Azimuth Parametersmentioning

confidence: 99%

Radar sensor based machine learning approach for precise vehicle position estimation

Sohail,

Khan,

Sandhu

et al. 2023

Sci Rep

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Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art.

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“…Naeem et al 35 proposed an Enhanced Sugeno Model Fuzzy Inference System‐Based Cluster Lifetime Protocol (ESMFISCLP) to maximize the stability of the network in terms of routing and mean throughput in vehicular networks. This ESMFISCLP model estimated the factors that included distance from the base station, concentration, degree of nodes, local distance, and residual energy for determining the potentiality of CHs in the network.…”

Section: Related Workmentioning

confidence: 99%

An intelligent hybrid prairie dog optimization algorithm‐based stable cluster reliable routing scheme for VANETs

Nithyanandam,

Ambiyaram,

Prabathkumar

2023

Int J Communication

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SummaryVehicular ad hoc network (VANET) is most significant for supporting intelligent transportation system (ITS)‐based technologies, but it gets hurdled by sparse distribution of vehicles on highways, and dynamically challenging topology that arises due to increase in traffic. Hence, energy stable and optimized cluster construction maximizes the network lifetime. In this paper, Hybrid Prairie Dogs and Beluga Whale Optimization‐based Node Clustering (HPDBWOA‐NC) mechanism is proposed with the parameters of highway route, node velocity, number of vehicular nodes, and communication for achieving stable cluster construction in VANETs. It is proposed with the balanced exploration and exploitation potential of Prairie Dog Optimization Algorithm (PDOA) and Beluga Whale Optimization Algorithm (BWOA) for establishing optimal clusters that increase the network stability during the routing process. It integrated the exploration and exploitation capabilities of PDOA and BWOA and confirmed better optimized clusters which confirmed reliable data delivery by preventing the issue of premature convergence. It constructed clusters and selected cluster heads (CHs) depending on the fitness factors of energy, interdistance between vehicles, communication range, and vehicular density. The results of the proposed HPDBWOA‐NC generated optimal number of CHs in the network which is comparatively 34.21% better than the benchmarked mechanisms. The mean throughput and packet delivery ratio (PDR) achieved by the proposed HPDBWOA‐NC are identified to be significantly improved by 25.48% and 28.91% better than the investigated metaheuristic clustering protocols. The statistical study also guaranteed an increased factor of 81, during the processing of optimizing the clusters during the employment of ITS applications in VANETs.

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Enhanced clustering based routing protocol in vehicular ad‐hoc networks

Cited by 22 publications

References 62 publications

Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

Traffic coordination by reducing jamming attackers in VANET using probabilistic Manhattan Grid Topology for automobile applications

Radar sensor based machine learning approach for precise vehicle position estimation

An intelligent hybrid prairie dog optimization algorithm‐based stable cluster reliable routing scheme for VANETs

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